Gongcheng Kexue Yu Jishu/Advanced Engineering Science

Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Science was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.


Submission Deadline
( Vol 56 , Issue 02 )
02 Mar 2024
Day
Hour
Min
Sec
Publish On
( Vol 56 , Issue 01 )
29 Feb 2024
Scopus Indexed (2024)

Aim and Scope

Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 20963246) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:

Agricultural science and engineering Section:

Horticulture, Agriculture, Soil Science, Agronomy, Biology, Economics, Biotechnology, Agricultural chemistry, Soil, development in plants, aromatic plants, subtropical fruits, Green house construction, Growth, Horticultural therapy, Entomology, Medicinal, Weed management in horticultural crops, plant Analysis, Tropical, Food Engineering, Venereal diseases, nutrient management, vegetables, Ophthalmology, Otorhinolaryngology, Internal Medicine, General Surgery, Soil fertility, Plant pathology, Temperate vegetables, Psychiatry, Radiology, Pulmonary Medicine, Dermatology, Organic farming, Production technology of fruits, Apiculture, Plant breeding, Molecular breeding, Recombinant technology, Plant tissue culture, Ornamental horticulture, Nursery techniques, Seed Technology, plantation crops, Food science and processing, cropping system, Agricultural Microbiology, environmental technology, Microbial, Soil and climatic factors, Crop physiology, Plant breeding,

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks.

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.
Latest Journals
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-14-10-2022-354

Abstract : The use of credit cards has grown significantly as the globe moves quickly toward digitization and cashless transactions. Additionally, there have been more fraud-related operations, which costs financial institutions a great deal of money. As a result, we must examine and distinguish between fraudulent and legitimate transactions. In this study, we wanted to implement the comprehensive model training procedure from beginning to end. As a result, we will have the best possible model to categorize the transaction into normal and aberrant varieties. Machine learning methods have been used in the fight against credit card fraud; however, fraud detection systems have yet to prove particularly effective. The relatively breakthrough of deep learning has been used in various fields to address complex challenges. In this study, we investigate several models for machine learning to spot fraudulent credit card activity. We compare the results produced by each model as well as their performance. The SMOTE methodology yields the most successful outcomes. It has been argued that under-sampling the majority class (the normal class) could effectively improve a classifier's sensitivity to the minority class. This research shows that, compared to just undersampling the majority class, the procedure we chose for oversampling the lower (abnormal) standard and undersampling the upper (normal) standard can enhance the analyzer's conduct..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-11-10-2022-353

Abstract : Warehouse automation is the automating practice of inventory control in-and-out of warehouses for consumers with zeroed human support. An enterprise can exclude labor-intensive costs which implicate Intelligent Storage System (ISS), Clustering and Racking Systems (CRS), and data analytics. The enterprise can run CRS manually, but it is extremely complex and can lead to an error-prone. This paper thus introduces the computer vision, upon Artificial Intelligence (AI) based training models with digital images and deploys to on-the-site devices that detect and interpret visual perception of products from cameras. Machine learning with visual AI recorded images of containers/products is used to equip the system with cognitive skills in the warehouse. In brief, it recognizes what to place into the warehouse, what checks out, where it is its cluster, and where it may have been relocated. This helps prevent problems arise in the racking system, where it could over-sit beyond expiry and become worthless. Both supervised and unsupervised training approaches for CRS with digital images are simulated. Finally, as a result, the computer vision speedily tracks warehouse products regardless of RFID tags, which subsides the limitations of RFID tags like vanished tags, imperfectly assigned tags, broken or non-functioning tags and the cost of re-issue tags..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-10-10-2022-352

Abstract : Many blind people around the world have had challenges reading books. To help them solve their problem and not depend on others, a writing method was developed by the inventor Louis Braille known as the Braille writing method. But with the development of technologies and computers and the need for an easier and less expensive way to help the blind solve the problem of reading any manuscript, even handwritten, and converting it into audio output. Therefore, in this paper we propose to design and implement a smart gara system to assist the visually impaired using a raspberry pi connected to a webcam interfaced that takes a picture of the text. With the use of OCR technology to convert an image to text through optical character recognition and extract text information from the image and convert it into speech. Then the sound is output through the speakers and amplifier. Whereas, the raspberry pi contains OCR (Optical Character Recognition) and a text to speech conversion unit (TTS engine), where the first performs optical character recognition and extracts text from the image, while the second converts it into speech and outputs it with headphones..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-10-10-2022-351

Abstract : Push-off tests are being used to experimentally investigate the shear performance of concrete construction joints as part of ongoing study to improve the bond between old-new concrete using reactive powder concrete and reinforcement. Using different types of interfacing, including use of water jet, dowel bars, grooved joint (triangle, rectangle, circle), In addition, cases without any construction joint were also considered as a reference. Shear strength, failure mechanisms, and load-displacement responses were studied as key parameters determining the shear performance of a single construction joint. Experimental results show that the grooved geometry greatly affect shear performance..
Full article
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-07-10-2022-349

Abstract : Zero-Shot Learning (ZSL) is a model training methodology where a model can predict at testing what is not learned during training. ZSL is effective when the features are learned from both images and text, mapped using different techniques to enable the model to categorize the images. ZSL is useful when we need to learn an intermediate semantic layer and an inference layer to predict the unseen classes during testing. ZSL also has several flavours of model learning. The feature learning can be broadly categorized into three in terms of the semantic space, the visual space, and the ZSL model space. The semantic space focuses on aligning the semantic attributes with class labels. The visual space extracts the features from the seen images using pre-trained networks. Transfer learning in semantic space and visual space is discussed in detail in this paper. The ZSL model space focuses on learning the relationship between the visual space and the semantic space. In inductive ZSL, only the data of the source classes are accessible during the training stage. But for the transductive ZSL strategies, both the labelled source data and the unlabelled target data are accessible for training. Generalized zero-shot learning is an extension of ZSL where the images that are to be predicted at test time contain both seen and unseen classes. This survey highlights the different hierarchies in the three areas and highlights the comparison between the different techniques and the future trends on ZSL..
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